scholarly journals Ordino: a visual cancer analysis tool for ranking and exploring genes, cell lines and tissue samples

2019 ◽  
Vol 35 (17) ◽  
pp. 3140-3142 ◽  
Author(s):  
Marc Streit ◽  
Samuel Gratzl ◽  
Holger Stitz ◽  
Andreas Wernitznig ◽  
Thomas Zichner ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Genomics ◽  
Cancer Cell Line ◽  
The Cancer Genome Atlas ◽  
Supplementary Information ◽  
Analysis Tool ◽  
Tissue Samples ◽  
Web Based ◽  
Prioritization Process ◽  
Cancer Genome Atlas

Abstract Summary Ordino is a web-based analysis tool for cancer genomics that allows users to flexibly rank, filter and explore genes, cell lines and tissue samples based on pre-loaded data, including The Cancer Genome Atlas, the Cancer Cell Line Encyclopedia and manually uploaded information. Interactive tabular data visualization that facilitates the user-driven prioritization process forms a core component of Ordino. Detail views of selected items complement the exploration. Findings can be stored, shared and reproduced via the integrated session management. Availability and implementation Ordino is publicly available at https://ordino.caleydoapp.org. The source code is released at https://github.com/Caleydo/ordino under the Mozilla Public License 2.0. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Ordino: a visual cancer analysis tool for ranking and exploring genes, cell lines, and tissue samples

2018 ◽  
Author(s):  
Marc Streit ◽  
Samuel Gratzl ◽  
Holger Stitz ◽  
Andreas Wernitznig ◽  
Thomas Zichner ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Genomics ◽  
Cancer Cell Line ◽  
The Cancer Genome Atlas ◽  
Analysis Tool ◽  
Tissue Samples ◽  
Web Based ◽  
Link Type ◽  
Prioritization Process ◽  
Cancer Genome Atlas

AbstractSummaryOrdino is a web-based analysis tool for cancer genomics that allows users to flexibly rank, filter, and explore genes, cell lines, and tissue samples based on pre-loaded data, including The Cancer Genome Atlas (TCGA), the Cancer Cell Line Encyclopedia (CCLE), and manually uploaded information. Interactive tabular data visualization that facilitates the user-driven prioritization process forms a core component of Ordino. Detail views of selected items complement the exploration. Findings can be stored, shared, and reproduced via the integrated session management.Availability and ImplementationOrdino is publicly available at https://ordino.caleydoapp.org. The source code is released at https://github.com/Caleydo/ordino under the Mozilla Public License [email protected]

MEpurity: estimating tumor purity using DNA methylation data

2019 ◽  
Author(s):  
Bowen Liu ◽  
Xiaofei Yang ◽  
Tingjie Wang ◽  
Jiadong Lin ◽  
Yongyong Kang ◽  
...  
Keyword(s):  
Fundamental Property ◽  
Cancer Cell Line ◽  
The Cancer Genome Atlas ◽  
Supplementary Information ◽  
Estimation Methods ◽  
Normal Sample ◽  
Cancer Data ◽  
Tumor Purity ◽  
Cancer Genome Atlas ◽  
Beta Mixture Model

Abstract Motivation Tumor purity is a fundamental property of each cancer sample and affects downstream investigations. Current tumor purity estimation methods either require matched normal sample or report moderately high tumor purity even on normal samples. It is critical to develop a novel computational approach to estimate tumor purity with sufficient precision based on tumor-only sample. Results In this study, we developed MEpurity, a beta mixture model-based algorithm, to estimate the tumor purity based on tumor-only Illumina Infinium 450k methylation microarray data. We applied MEpurity to both The Cancer Genome Atlas (TCGA) cancer data and cancer cell line data, demonstrating that MEpurity reports low tumor purity on normal samples and comparable results on tumor samples with other state-of-art methods. Availability and implementation MEpurity is a C++ program which is available at https://github.com/xjtu-omics/MEpurity. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Comparing cancer cell lines and tumor samples by genomic profiles

2015 ◽  
Author(s):  
Rileen Sinha ◽  
Nikolaus Schultz ◽  
Chris Sander
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Line ◽  
Cancer Cell Lines ◽  
Response To Therapy ◽  
Computational Method ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Tumor Material

Cancer cell lines are often used in laboratory experiments as models of tumors, although they can have substantially different genetic and epigenetic profiles compared to tumors. We have developed a general computational method, TumorComparer, to systematically quantify similarities and differences between tumor material when detailed genetic and molecular profiles are available. The comparisons can be flexibly tailored to a particular biological question by placing a higher weight on functional alterations of interest (weighted similarity). In a first pan-cancer application, we have compared 260 cell lines from the Cancer Cell Line Encyclopaedia (CCLE) and 1914 tumors of six different cancer types from The Cancer Genome Atlas (TCGA), using weights to emphasize genomic alterations that frequently recur in tumors. We report the potential suitability of particular cell lines as tumor models and identify apparently unsuitable outlier cell lines, some of which are in wide use, for each of the six cancer types. In future, this weighted similarity method may be generalized for use in a clinical setting to compare patient profiles consisting of genomic patterns combined with clinical attributes, such as diagnosis, treatment and response to therapy.

scholarly journals Coral: a web-based visual analysis tool for creating and characterizing cohorts

2021 ◽  
Author(s):  
Patrick Adelberger ◽  
Klaus Eckelt ◽  
Markus J. Bauer ◽  
Marc Streit ◽  
Christian Haslinger ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Cohort Analysis ◽  
The Cancer Genome Atlas ◽  
Analysis Tool ◽  
Main Task ◽  
Web Based ◽  
The Public ◽  
Genomic Markers ◽  
Cancer Genome Atlas ◽  
Response Biomarkers

Summary: A main task in computational cancer analysis is the identification of patient subgroups (i.e., cohorts) based on metadata attributes (patient stratification) or genomic markers of response (biomarkers). Coral is a web-based cohort analysis tool that is designed to support this task: Users can interactively create and refine cohorts, which can then be compared, characterized, and inspected down to the level of single items. We visualize the evolution of cohorts and also provide intuitive access to prevalence information. Coral can be utilized to explore any type of cohort and sample set. Here, we focus on the analysis of genomic data from cancer patients and therefore included in the public version data from the AACR Project GENIE, The Cancer Genome Atlas, and the Cell Line Encyclopedia. Availability and Implementation: Coral is publicly available at https://coral.caleydoapp.org. The source code is released at https://github.com/Caleydo/coral.

scholarly journals TRIM27 interacts with Iκbα to promote the growth of human renal cancer cells through regulating the NF-κB pathway

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chengwu Xiao ◽  
Wei Zhang ◽  
Meimian Hua ◽  
Huan Chen ◽  
Bin Yang ◽  
...  
Keyword(s):  
Cell Lines ◽  
Prognostic Indicator ◽  
The Cancer Genome Atlas ◽  
Mrna Levels ◽  
Loss Of Function ◽  
Protein Levels ◽  
Kaplan Meier ◽  
Cancer Genome Atlas

Abstract Background The tripartite motif (TRIM) family proteins exhibit oncogenic roles in various cancers. The roles of TRIM27, a member of the TRIM super family, in renal cell carcinoma (RCC) remained unexplored. In the current study, we aimed to investigate the clinical impact and roles of TRIM27 in the development of RCC. Methods The mRNA levels of TRIM27 and Kaplan–Meier survival of RCC were analyzed from The Cancer Genome Atlas database. Real-time PCR and Western blotting were used to measure the mRNA and protein levels of TRIM27 both in vivo and in vitro. siRNA and TRIM27 were exogenously overexpressed in RCC cell lines to manipulate TRIM27 expression. Results We discovered that TRIM27 was elevated in RCC patients, and the expression of TRIM27 was closely correlated with poor prognosis. The loss of function and gain of function results illustrated that TRIM27 promotes cell proliferation and inhibits apoptosis in RCC cell lines. Furthermore, TRIM27 expression was positively associated with NF-κB expression in patients with RCC. Blocking the activity of NF-κB attenuated the TRIM27-mediated enhancement of proliferation and inhibition of apoptosis. TRIM27 directly interacted with Iκbα, an inhibitor of NF-κB, to promote its ubiquitination, and the inhibitory effects of TRIM27 on Iκbα led to NF-κB activation. Conclusions Our results suggest that TRIM27 exhibits an oncogenic role in RCC by regulating NF-κB signaling. TRIM27 serves as a specific prognostic indicator for RCC, and strategies targeting the suppression of TRIM27 function may shed light on future therapeutic approaches.

scholarly journals Effect of Different Expression of Immune-Related lncRNA on Colon Adenocarcinoma and Its Relation to Prognosis

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Meiwei Mu ◽  
Yi Tang ◽  
Zheng Yang ◽  
Yuling Qiu ◽  
Xiaohong Li ◽  
...  
Keyword(s):  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Future Research ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Gene Sets ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Objective. To explore the expression of immune-related lncRNAs in colon adenocarcinoma and find out the effect on how these lncRNAs influence the development and prognosis of colon adenocarcinoma. Method. Transcriptome data of colon adenocarcinoma from The Cancer Genome Atlas (TCGA) were downloaded, and gene sets “IMMUNE RESPONSE” and “IMMUNE SYSTEM PROCESS” were sought from the Molecular Signatures Database (MSigDB). The expression of immune-related genes was extracted that were immune-related mRNAs. Then, the immune-related lncRNAs were sought out by utilizing of the above data. Clinical traits were combined with immune-related lncRNAs, so that prognostic-related lncRNAs were identified by Cox regression. Multivariate Cox regression was built to calculate risk scores. Relationships between clinical traits and immune-related lncRNAs were also calculated. Result. A total of 480 colorectal adenocarcinoma patients and 41 normal control patients’ transcriptome sequencing data of tissue samples were obtained from TCGA database. 918 immune-related lncRNAs were screened. Cox regression showed that 34 immune-related lncRNAs were associated with colon adenocarcinoma prognosis. Seven lncRNAs were independent risk factors. Conclusion. This study revealed that some lncRNAs can affect the development and prognosis of colon adenocarcinoma. It may provide new theory evidence of molecular mechanism for the future research and molecular targeted therapy of colon adenocarcinoma.

scholarly journals Identification of a Novel Epithelial-Mesenchymal Transition Gene Signature Regulated by KEAP1-NRF2 Pathway in Esophageal Carcinoma

2020 ◽  
Author(s):  
Mohamed Elshaer ◽  
Ahmed Hammad ◽  
Xiu Jun Wang ◽  
Xiuwen Tang
Keyword(s):  
Cell Lines ◽  
Epithelial Mesenchymal Transition ◽  
Cancer Cell Line ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Mesenchymal Transition ◽  
Gene Set ◽  
Over Expression

Abstract BackgroundKEAP1-NRF2 pathway alterations were identified in many cancers including, esophageal cancer (ESCA). Identifying biomarkers that are associated with mutations in this pathway will aid in defining this cancer subset; and hence in supporting precision and personalized medicine. MethodsIn this study, 182 tumor samples from the Cancer Genome Atlas (TCGA)-ESCA RNA-Seq V2 level 3 data were segregated into two groups KEAP1-NRF2-mutated (22) and wild-type (160).The two groups were subjected to differential gene expression analysis, and we performed Gene Set Enrichment Analysis (GSEA) to determine all significantly affected biological pathways. Then, the enriched gene set was integrated with the differentially expressed genes (DEGs) to identify a gene signature regulated by the KEAP1-NRF2 pathway in ESCA. Furthermore, we validated the gene signature using mRNA expression data of ESCA cell lines provided by the Cancer Cell Line Encyclopedia (CCLE). The identified signature was tested in 3 independent ESCA datasets to assess its prognostic value.ResultsWe identified 11 epithelial-mesenchymal transition (EMT) genes regulated by the KEAP1-NRF2 pathway in ESCA patients. Five of the 11 genes showed significant over-expression in KEAP1-NRF2-mutated ESCA cell lines. In addition, the over-expression of these five genes was significantly associated with poor survival in 3 independent ESCA datasets, including the TCGA-ESCA dataset.ConclusionAltogether, we identified a novel EMT 5-gene signature regulated by the KEAP1-NRF2 axis and this signature is strongly associated with metastasis and drug resistance in ESCA. These 5-genes are potential biomarkers and therapeutic targets for ESCA patients in whom the KEAP1-NRF2 pathway is altered.

Prediction of transcription factors binding events based on epigenetic modifications in different human cells

Epigenomics ◽  
2020 ◽  
Vol 12 (16) ◽  
pp. 1443-1456
Author(s):  
Yan Huang ◽  
Dianshuang Zhou ◽  
Yihan Wang ◽  
Xingda Zhang ◽  
Mu Su ◽  
...  
Keyword(s):  
Cell Lines ◽  
Predictive Models ◽  
High Performance ◽  
Epigenetic Modifications ◽  
The Cancer Genome Atlas ◽  
Encode Project ◽  
Atlas Data ◽  
Random Forest Method ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Aim: We aim to predict transcription factor (TF) binding events from knowledge of gene expression and epigenetic modifications. Materials & methods: TF-binding events based on the Encode project and The Cancer Genome Atlas data were analyzed by the random forest method. Results: We showed the high performance of TF-binding predictive models in GM12878, HeLa, HepG2 and K562 cell lines and applied them to other cell lines and tissues. The genes bound by the top TFs ( MAX and MAZ) were significantly associated with cancer-related processes such as cell proliferation and DNA repair. Conclusion: We successfully constructed TF-binding predictive models in cell lines and applied them in tissues.

scholarly journals PABPC1 relevant bioinformatic profiling and prognostic value in gliomas

2020 ◽  
Vol 16 (1) ◽  
pp. 4279-4288 ◽  
Author(s):  
Qiangwei Wang ◽  
Zhiliang Wang ◽  
Zhaoshi Bao ◽  
Chuanbao Zhang ◽  
Zheng Wang ◽  
...  
Keyword(s):  
Biological Process ◽  
The Cancer Genome Atlas ◽  
Analysis Tool ◽  
Expression Data ◽  
Clinical Value ◽  
Mrna Expression Data ◽  
R Language ◽  
Molecular Features ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Aim: We aimed at investigating molecular features and potential clinical value of PABPC1 in gliomas. Materials & methods: We assembled totally 1000 glioma samples with mRNA expression data from Chinese Glioma Genome Atlas and The Cancer Genome Atlas. We utilized R language as the main analysis tool. Gene Ontology was performed for functional analysis. Results: PABPC1 was downregulated in gliomas with higher malignance and PABPC1 may contribute as potential predictor of proneural subtype in gliomas. Higher expression of PABPC1 was significantly related to better prognosis and related to biological process of translation. Conclusion: Our finding improves the understanding of PABPC1 as a novel biomarker with potential therapeutic connotations.

scholarly journals A proteomic approach to understand the clinical significance of acute myeloid leukemia-derived extracellular vesicles reflecting essential characteristics of leukemia

2020 ◽  
pp. mcp.RA120.002169
Author(s):  
Ka-Won Kang ◽  
Hyoseon Kim ◽  
Woojune Hur ◽  
Jik-han Jung ◽  
Su Jin Jeong ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Cell Lines ◽  
Myeloid Leukemia ◽  
Extracellular Vesicle ◽  
The Cancer Genome Atlas ◽  
Enzyme Linked Immunosorbent Assay ◽  
Expression Levels ◽  
Proteomic Approach ◽  
Cancer Genome Atlas ◽  
Acute Myeloid

Extracellular vesicle (EV) proteins from acute myeloid leukemia (AML) cell lines were analyzed using mass spectrometry. The analyses identified 2450 proteins, including 461 differentially expressed proteins (290 upregulated and 171 downregulated). CD53 and CD47 were upregulated and were selected as candidate biomarkers. The association between survival of patients with AML and the expression levels of CD53 and CD47 at diagnosis was analyzed using mRNA expression data from The Cancer Genome Atlas database. Patients with higher expression levels showed significantly inferior survival than those with lower expression levels. Enzyme-linked immunosorbent assay results of the expression levels of CD53 and CD47 from EVs in the bone marrow of patients with AML at diagnosis and at the time of complete remission with induction chemotherapy revealed that patients with downregulated CD53 and CD47 expression appeared to relapse less frequently. Network model analysis of EV proteins revealed several upregulated kinases, including LYN, CSNK2A1, SYK, CSK, and PTK2B. The potential cytotoxicity of several clinically applicable drugs that inhibit these kinases was tested in AML cell lines. The drugs lowered the viability of AML cells. The collective data suggest that AML-derived EVs could reflect essential leukemia biology.

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